Sensing with the Motor Cortex

The primary motor cortex is a critical node in the network of brain regions responsible for voluntary motor behavior. It has been less appreciated, however, that the motor cortex exhibits sensory responses in a variety of modalities including vision and somatosensation. We review current work that emphasizes the heterogeneity in sensorimotor responses in the motor cortex and focus on its implications for cortical control of movement as well as for brain-machine interface development

Improved spike-based brain-machine interface using bayesian adaptive kernel smoother and deep learning

Multiunit activity (MUA) has been proposed to mitigate the robustness issue faced by single-unit activity (SUA)-based brain-machine interfaces (BMIs). Most MUA-based BMIs still employ a binning method for estimating firing rates and linear decoder for decoding behavioural parameters. The limitations of binning and linear decoder lead to suboptimal performance of MUA-based BMIs. To address this issue, we propose a method which consists of Bayesian adaptive kernel smoother (BAKS) as the firing rate estimation algorithm and deep learning, particularly quasi-recurrent neural network (QRNN), as the decoding algorithm. We evaluated the proposed method for reconstructing (offline) hand kinematics from intracortical neural data chronically recorded from the primary motor cortex of two non-human primates. Extensive empirical results across recording sessions and subjects showed that the proposed method consistently outperforms other combinations of firing rate estimation algorithm and decoding algorithm. Overall results suggest the effectiveness of the proposed method for improving the decoding performance of MUA-based BMIs

Real-Time Decision Fusion for Multimodal Neural Prosthetic Devices

The field of neural prosthetics aims to develop prosthetic limbs with a brain-computer interface (BCI) through which neural activity is decoded into movements. A natural extension of current research is the incorporation of neural activity from multiple modalities to more accurately estimate the user's intent. The challenge remains how to appropriately combine this information in real-time for a neural prosthetic device., i.e., fusing predictions from several single-modality decoders to produce a more accurate device state estimate. We examine two algorithms for continuous variable decision fusion: the Kalman filter and artificial neural networks (ANNs). Using simulated cortical neural spike signals, we implemented several successful individual neural decoding algorithms, and tested the capabilities of each fusion method in the context of decoding 2-dimensional endpoint trajectories of a neural prosthetic arm. Extensively testing these methods on random trajectories, we find that on average both the Kalman filter and ANNs successfully fuse the individual decoder estimates to produce more accurate predictions.Our results reveal that a fusion-based approach has the potential to improve prediction accuracy over individual decoders of varying quality, and we hope that this work will encourage multimodal neural prosthetics experiments in the future

Uniform and Non-uniform Perturbations in Brain-Machine Interface Task Elicit Similar Neural Strategies

abstract: The neural mechanisms that take place during learning and adaptation can be directly probed with brain-machine interfaces (BMIs). We developed a BMI controlled paradigm that enabled us to enforce learning by introducing perturbations which changed the relationship between neural activity and the BMI's output. We introduced a uniform perturbation to the system, through a visuomotor rotation (VMR), and a non-uniform perturbation, through a decorrelation task. The controller in the VMR was essentially unchanged, but produced an output rotated at 30° from the neurally specified output. The controller in the decorrelation trials decoupled the activity of neurons that were highly correlated in the BMI task by selectively forcing the preferred directions of these cell pairs to be orthogonal. We report that movement errors were larger in the decorrelation task, and subjects needed more trials to restore performance back to baseline. During learning, we measured decreasing trends in preferred direction changes and cross-correlation coefficients regardless of task type. Conversely, final adaptations in neural tunings were dependent on the type controller used (VMR or decorrelation). These results hint to the similar process the neural population might engage while adapting to new tasks, and how, through a global process, the neural system can arrive to individual solutions.View the article as published at http://journal.frontiersin.org/article/10.3389/fnsys.2016.00070/ful

Lesson Learnt and Future of AI Applied to Manufacturing

This chapter touches on several aspects related to the role of Artificial Intelligence (AI) and Machine Learning (ML) in the manufacturing sector, and is split in different sub-chapters, focusing on specific new technology enablers that have the potential of solving or minimizing known issues in the manufacturing and, more in general, in the Industrial Internet of Things (IIoT) domain. After introducing AI/ML as a technology enabler for the IoT in general and for manufacturing in particular, the next four sections detail two key technology enablers (EdgeML and federated learning scenarios, challenges and tools), one most important area of the IoT system that needs to decrease energy consumption and increase reliability (reduce receiver Processing complexity and enhancing reliability through multi-connectivity uplink connections), and finally a glimpse at the future describing a promising new technology (Embodied AI), its link with millimetre waves connectivity and potential business impact